LLM Compressor: Quantization File Size Larger Than Expected?

Hey there! Let's dive into an interesting issue that pops up when you're working with model quantization, specifically with the LLM Compressor library and the meta-llama/Llama-3.2-1B model. If you've ever quantized a model and noticed the resulting file size is bigger than you anticipated, you're not alone. This discussion will explore why this might happen, and what it could mean for your quantized model.

The Core Problem: Larger-Than-Expected File Sizes

The user is experiencing a discrepancy in file sizes after quantizing the meta-llama/Llama-3.2-1B model using the LLM Compressor. They're using the W4A16 quantization scheme, which is designed to reduce the model's size by storing weights in a 4-bit format. Here's a quick rundown of the scenario:

• Original Setup: They're using LLM Compressor (v0.x) with an AWQModifier to target Linear layers, excluding lm_head. The code snippet looks something like this:

  AWQModifier(
      ignore=["lm_head"],
      scheme="W4A16",
      targets=["Linear"],
  )
  

• The Unexpected Result: After running the quantization and saving the model, the user ends up with two files: model.safetensors and pytorch_model.bin, both clocking in around 1.44 GB. However, when using AutoAWQ with the same W4A16 scheme on the same model, the file size is significantly smaller, around 1 GB. This difference sparks the main question: why is the file size so much bigger with the LLM Compressor?

Is the File Size of 1.44 GB Expected for W4A16 Quantization?

The central question is, is a 1.44 GB file size normal for a 1B-parameter model quantized using W4A16 with LLM Compressor? The answer isn't a simple yes or no; it depends on a few factors. Let's break down the possible reasons for the larger file size:

1. Partial Quantization: It's possible that the weights aren't fully packed into the 4-bit format. This could happen if some layers or tensors aren't correctly quantized, leading to them being stored in a higher-precision format like FP16 or FP32, which would drastically increase the file size.
2. Additional Metadata and Full-Precision Tensors: The model might be saving more than just the quantized weights. Additional metadata, such as scaling factors, or even full-precision tensors could be saved alongside the compressed weights. This metadata is essential for dequantization and inference but adds to the overall file size.
3. Redundant Copies or Storage Issues: There's a possibility that the save_pretrained function might be storing redundant copies of the model's weights. This could involve saving both the original and the quantized weights simultaneously, which would inflate the file size significantly. This isn't the expected behavior, but it's a potential cause that needs investigation.

Diving Deeper: Investigating the Root Cause

To determine the exact cause of the larger file size, a few steps can be taken:

• Inspect the Saved Files: Examine the contents of the model.safetensors and pytorch_model.bin files. Tools like safetensors_rust or huggingface_hub can help you inspect the tensors stored inside these files and their data types. This will reveal if any tensors are stored in higher precision than expected.
• Verify Quantization Coverage: Ensure that all the intended layers have been quantized. Check the logs from the quantization process for any warnings or errors that indicate that certain layers were skipped or failed to quantize. This can help verify if the ignore parameters are working as intended.
• Compare with AutoAWQ: Since AutoAWQ produces a smaller file, comparing the output of both tools can provide valuable insights. Analyze the structure and contents of the AutoAWQ-generated model to understand what's different in terms of storage and metadata.
• Check the save_pretrained Parameters: Double-check the parameters used when calling model.save_pretrained. Make sure no options are inadvertently causing redundant data to be saved.
• Test Inference: While the model reloads and generates correctly, running thorough inference tests is crucial. Compare the performance (speed and accuracy) of the LLM Compressor-quantized model with the original and the AutoAWQ-quantized model. This helps ensure that the larger file size doesn't come at the cost of performance.

Troubleshooting and Optimizing for File Size

If you find that the file size is larger than expected, here's how you can troubleshoot and potentially optimize it:

• Verify the Quantization Process: Ensure that all the layers are being quantized and that the process doesn't throw any errors. Correctly setting up the AWQModifier is crucial.
• Check for Unnecessary Metadata: Review the saving process to see if any extra metadata is being saved, and consider removing any redundant information.
• Experiment with Different Settings: Try different quantization schemes and configurations. It's possible that a different approach yields better compression. Consider testing other quantization methods available within LLM Compressor.
• Update Libraries: Make sure you're using the latest versions of LLM Compressor and its dependencies. Updates often include bug fixes and improvements that could impact file size and compression efficiency.
• Consult the Documentation: Review the documentation for LLM Compressor. It may contain guidance on optimizing file sizes and understanding the expected storage behavior.

Conclusion: Understanding the Trade-offs

The larger file size observed when using LLM Compressor with W4A16 quantization compared to AutoAWQ is a valid concern. It’s important to understand the reasons behind this discrepancy. While the model seems to load and generate correctly, investigating the root cause ensures that you’re getting the expected benefits of quantization, namely reduced file size and potential acceleration.

By carefully examining the contents of the saved files, verifying the quantization process, and experimenting with different settings, you can optimize your model's file size. Remember that the goal is not only to reduce the file size but also to maintain performance and accuracy. Striking the right balance is key to successful model quantization.

In summary, the file size of 1.44 GB for a 1B-parameter model quantized with W4A16 is not typical. Investigate the saved tensors, quantization coverage, and saving parameters to identify the cause. By carefully examining these aspects, you can ensure that the quantization process is working as expected and that you’re getting the compression you need.

For more information on model quantization, and related topics, check out these trusted resources:

• Hugging Face Documentation: Explore their extensive documentation on model quantization. Here, you'll find great detail on the different quantization techniques and how they are used within their ecosystem.

• AutoAWQ Github Repository: This repository provides valuable insights into how AutoAWQ quantization functions, and how it differs from other approaches. See: AutoAWQ

These resources are great for a deeper understanding of the concepts discussed in this article.

Happy Quantizing!